# Modified by Lang Huang (laynehuang@outlook.com)
# All rights reserved.

# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
# --------------------------------------------------------
# References:
# Swin Transformer: https://github.com/microsoft/Swin-Transformer
import torch
import torch.nn as nn
from torch.nn import functional as F
import torch.utils.checkpoint as checkpoint

from timm.models.layers import DropPath, to_2tuple, trunc_normal_

from .base_green_models import BaseGreenModel
from .group_window_attention import WindowAttention, GroupingModule, get_coordinates


class Mlp(nn.Module):
    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
        super().__init__()
        out_features = out_features or in_features
        hidden_features = hidden_features or in_features
        self.fc1 = nn.Linear(in_features, hidden_features)
        self.act = act_layer()
        self.fc2 = nn.Linear(hidden_features, out_features)
        self.drop = nn.Dropout(drop)

    def forward(self, x):
        x = self.fc1(x)
        x = self.act(x)
        x = self.drop(x)
        x = self.fc2(x)
        x = self.drop(x)
        return x

class SwinTransformerBlock(nn.Module):
    r""" Swin Transformer Block.

    Args:
        dim (int): Number of input channels.
        input_resolution (tuple[int]): Input resulotion.
        num_heads (int): Number of attention heads.
        window_size (int): Window size.
        shift_size (int): Shift size for SW-MSA.
        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
        drop (float, optional): Dropout rate. Default: 0.0
        attn_drop (float, optional): Attention dropout rate. Default: 0.0
        drop_path (float, optional): Stochastic depth rate. Default: 0.0
        act_layer (nn.Module, optional): Activation layer. Default: nn.GELU
        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
    """

    def __init__(self, dim, input_resolution, num_heads, window_size=7, shift_size=0,
                 mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., drop_path=0.,
                 act_layer=nn.GELU, norm_layer=nn.LayerNorm):
        super().__init__()
        self.dim = dim
        self.input_resolution = input_resolution
        self.num_heads = num_heads
        self.window_size = window_size
        self.shift_size = shift_size
        self.mlp_ratio = mlp_ratio
        if min(self.input_resolution) <= self.window_size:
            # if window size is larger than input resolution, we don't partition windows
            self.shift_size = 0
            self.window_size = min(self.input_resolution)
        assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size"

        self.norm1 = norm_layer(dim)
        self.attn = WindowAttention(
            dim, window_size=to_2tuple(self.window_size), num_heads=num_heads,
            qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)

        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
        self.norm2 = norm_layer(dim)
        mlp_hidden_dim = int(dim * mlp_ratio)
        self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)

    def forward(self, x, attn_mask, rel_pos_idx):
        shortcut = x
        x = self.norm1(x)

        # W-MSA/SW-MSA
        x = self.attn(x, mask=attn_mask, pos_idx=rel_pos_idx)  # B*nW, N_vis, C

        # FFN
        x = shortcut + self.drop_path(x)
        x = x + self.drop_path(self.mlp(self.norm2(x)))

        return x

    def extra_repr(self) -> str:
        return f"dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, " \
               f"window_size={self.window_size}, shift_size={self.shift_size}, mlp_ratio={self.mlp_ratio}"


class PatchMerging(nn.Module):
    r""" Patch Merging Layer.

    Args:
        input_resolution (tuple[int]): Resolution of input feature.
        dim (int): Number of input channels.
        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
    """

    def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm):
        super().__init__()
        self.input_resolution = input_resolution
        self.dim = dim
        self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
        self.norm = norm_layer(4 * dim)

    def forward(self, x, coords_prev, mask_prev):
        """
        x: B, H*W, C
        """
        H, W = self.input_resolution
        B, L, C = x.shape
        assert H % 2 == 0 and W % 2 == 0, f"x size ({H}*{W}) are not even."

        # gather patches lie within 2x2 local window
        mask = mask_prev.reshape(H//2, 2, W//2, 2).permute(0, 2, 1, 3).reshape(-1)
        coords = get_coordinates(H, W, device=x.device).reshape(2, -1).permute(1, 0)
        coords = coords.reshape(H//2, 2, W//2, 2, 2).permute(0, 2, 1, 3, 4).reshape(-1, 2)
        coords_vis_local = coords[mask].reshape(-1, 2)
        coords_vis_local = coords_vis_local[:, 0] * H + coords_vis_local[:, 1]
        idx_shuffle = torch.argsort(torch.argsort(coords_vis_local))

        x = torch.index_select(x, 1, index=idx_shuffle)
        x = x.reshape(B, L//4, 4, C)
        # row-first order to column-first order
        # make it compatible with Swin (https://github.com/microsoft/Swin-Transformer/blob/main/models/swin_transformer.py#L342)
        x = torch.cat([x[:, :, 0], x[:, :, 2], x[:, :, 1], x[:, :, 3]], dim=-1)
        
        # merging by a linear layer
        x = self.norm(x)
        x = self.reduction(x)

        mask_new = mask_prev.view(1, H//2, 2, W//2, 2).sum(dim=(2, 4))
        assert torch.unique(mask_new).shape[0] == 2
        mask_new = (mask_new > 0).reshape(1, -1)
        coords_new = get_coordinates(H//2, W//2, x.device).reshape(1, 2, -1)
        coords_new = coords_new.transpose(2, 1)[mask_new].reshape(1, -1, 2)
        return x, coords_new, mask_new

    def extra_repr(self) -> str:
        return f"input_resolution={self.input_resolution}, dim={self.dim}"


class BasicLayer(nn.Module):
    """ A basic Swin Transformer layer for one stage.

    Args:
        dim (int): Number of input channels.
        input_resolution (tuple[int]): Input resolution.
        depth (int): Number of blocks.
        num_heads (int): Number of attention heads.
        window_size (int): Local window size.
        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
        drop (float, optional): Dropout rate. Default: 0.0
        attn_drop (float, optional): Attention dropout rate. Default: 0.0
        drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
        norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
        downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
    """

    def __init__(self, dim, input_resolution, depth, num_heads, window_size,
                 mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0.,
                 drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False):

        super().__init__()
        self.dim = dim
        self.input_resolution = input_resolution
        self.depth = depth
        self.use_checkpoint = use_checkpoint
        self.window_size = window_size
        if min(self.input_resolution) <= self.window_size:
            # if window size is larger than input resolution, we don't partition windows
            self.shift_size = 0
            self.window_size = min(self.input_resolution)
        else:
            self.shift_size = window_size // 2

        # build blocks
        self.blocks = nn.ModuleList([
            SwinTransformerBlock(dim=dim, input_resolution=input_resolution,
                                 num_heads=num_heads, window_size=window_size,
                                 shift_size=0 if (i % 2 == 0) else window_size // 2,
                                 mlp_ratio=mlp_ratio,
                                 qkv_bias=qkv_bias, qk_scale=qk_scale,
                                 drop=drop, attn_drop=attn_drop,
                                 drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
                                 norm_layer=norm_layer)
            for i in range(depth)])

        # patch merging layer
        if downsample is not None:
            self.downsample = downsample(input_resolution, dim=dim, norm_layer=norm_layer)
        else:
            self.downsample = None
    

    def forward(self, x, coords, patch_mask):
        # prepare the attention mask and relative position bias
        group_block = GroupingModule(self.window_size, 0)
        mask, pos_idx = group_block.prepare(coords, num_tokens=x.shape[1])
        if self.window_size < min(self.input_resolution):
            group_block_shift = GroupingModule(self.window_size, self.shift_size)
            mask_shift, pos_idx_shift = group_block_shift.prepare(coords, num_tokens=x.shape[1])
        else:
            # do not shift
            group_block_shift = group_block
            mask_shift, pos_idx_shift = mask, pos_idx

        # forward with grouping/masking
        for i, blk in enumerate(self.blocks):
            gblk = group_block if i % 2 ==0 else group_block_shift
            attn_mask = mask if i % 2 ==0 else mask_shift
            rel_pos_idx = pos_idx if i % 2 ==0 else pos_idx_shift
            x = gblk.group(x)
            if self.use_checkpoint:
                x = checkpoint.checkpoint(blk, x, attn_mask, rel_pos_idx)
            else:
                x = blk(x, attn_mask, rel_pos_idx)
            x = gblk.merge(x)
        
        # patch merging
        if self.downsample is not None:
            x, coords, patch_mask = self.downsample(x, coords, patch_mask)
        
        return x, coords, patch_mask


    def extra_repr(self) -> str:
        return f"dim={self.dim}, input_resolution={self.input_resolution}, window_size={self.window_size},"\
                f"shift_size={self.shift_size}, depth={self.depth}"


class PatchEmbed(nn.Module):
    r""" Image to Patch Embedding

    Args:
        img_size (int): Image size.  Default: 224.
        patch_size (int): Patch token size. Default: 4.
        in_chans (int): Number of input image channels. Default: 3.
        embed_dim (int): Number of linear projection output channels. Default: 96.
        norm_layer (nn.Module, optional): Normalization layer. Default: None
    """

    def __init__(self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):
        super().__init__()
        img_size = to_2tuple(img_size)
        patch_size = to_2tuple(patch_size)
        patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]]
        self.img_size = img_size
        self.patch_size = patch_size
        self.patches_resolution = patches_resolution
        self.num_patches = patches_resolution[0] * patches_resolution[1]

        self.in_chans = in_chans
        self.embed_dim = embed_dim

        self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
        if norm_layer is not None:
            self.norm = norm_layer(embed_dim)
        else:
            self.norm = None

    def forward(self, x):
        B, C, H, W = x.shape
        # FIXME look at relaxing size constraints
        assert H == self.img_size[0] and W == self.img_size[1], \
            f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
        x = self.proj(x).flatten(2).transpose(1, 2)  # B Ph*Pw C
        if self.norm is not None:
            x = self.norm(x)
        return x


class SwinTransformer(BaseGreenModel):
    r""" Swin Transformer
        A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows`  -
          https://arxiv.org/pdf/2103.14030

    Args:
        img_size (int | tuple(int)): Input image size. Default 224
        patch_size (int | tuple(int)): Patch size. Default: 4
        in_chans (int): Number of input image channels. Default: 3
        num_classes (int): Number of classes for classification head. Default: 1000
        embed_dim (int): Patch embedding dimension. Default: 96
        depths (tuple(int)): Depth of each Swin Transformer layer.
        num_heads (tuple(int)): Number of attention heads in different layers.
        window_size (int): Window size. Default: 7
        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4
        qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
        qk_scale (float): Override default qk scale of head_dim ** -0.5 if set. Default: None
        drop_rate (float): Dropout rate. Default: 0
        attn_drop_rate (float): Attention dropout rate. Default: 0
        drop_path_rate (float): Stochastic depth rate. Default: 0.1
        norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
        ape (bool): If True, add absolute position embedding to the patch embedding. Default: False
        patch_norm (bool): If True, add normalization after patch embedding. Default: True
        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False
    """

    def __init__(self, img_size=224, patch_size=4, in_chans=3, num_classes=1000,
                 embed_dim=96, depths=[2, 2, 6, 2], num_heads=[3, 6, 12, 24],
                 window_size=7, mlp_ratio=4., qkv_bias=True, qk_scale=None,
                 drop_rate=0., attn_drop_rate=0., drop_path_rate=0.1,
                 norm_layer=nn.LayerNorm, ape=False, patch_norm=True,
                 use_checkpoint=False):
        super().__init__()

        self.num_classes = num_classes
        self.num_layers = len(depths)
        self.embed_dim = embed_dim
        self.ape = ape
        self.patch_norm = patch_norm
        self.num_features = int(embed_dim * 2 ** (self.num_layers - 1))
        self.mlp_ratio = mlp_ratio
        self.drop_path_rate = drop_path_rate

        # split image into non-overlapping patches
        self.patch_embed = PatchEmbed(
            img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim,
            norm_layer=norm_layer if self.patch_norm else None)
        num_patches = self.patch_embed.num_patches
        patches_resolution = self.patch_embed.patches_resolution
        self.patches_resolution = patches_resolution

        # absolute position embedding
        if self.ape:
            self.absolute_pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim))
            trunc_normal_(self.absolute_pos_embed, std=.02)

        self.pos_drop = nn.Dropout(p=drop_rate)

        # stochastic depth
        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))]  # stochastic depth decay rule

        # build layers
        self.layers = nn.ModuleList()
        for i_layer in range(self.num_layers):
            layer = BasicLayer(dim=int(embed_dim * 2 ** i_layer),
                               input_resolution=(patches_resolution[0] // (2 ** i_layer),
                                                 patches_resolution[1] // (2 ** i_layer)),
                               depth=depths[i_layer],
                               num_heads=num_heads[i_layer],
                               window_size=window_size,
                               mlp_ratio=self.mlp_ratio,
                               qkv_bias=qkv_bias, qk_scale=qk_scale,
                               drop=drop_rate, attn_drop=attn_drop_rate,
                               drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])],
                               norm_layer=norm_layer,
                               downsample=PatchMerging if (i_layer < self.num_layers - 1) else None,
                               use_checkpoint=use_checkpoint)
            self.layers.append(layer)

        self.norm = norm_layer(self.num_features)

        self.apply(self._init_weights)

    def _init_weights(self, m):
        if isinstance(m, nn.Linear):
            trunc_normal_(m.weight, std=.02)
            if isinstance(m, nn.Linear) and m.bias is not None:
                nn.init.constant_(m.bias, 0)
        elif isinstance(m, nn.LayerNorm):
            nn.init.constant_(m.bias, 0)
            nn.init.constant_(m.weight, 1.0)

    @torch.jit.ignore
    def no_weight_decay(self):
        return {'absolute_pos_embed'}

    @torch.jit.ignore
    def no_weight_decay_keywords(self):
        return {'relative_position_bias_table'}

    def patchify(self, x):
        # patch embedding
        x = self.patch_embed(x)
        if self.ape:
            x = x + self.absolute_pos_embed
        x = self.pos_drop(x)
        return x
    
    def forward_features(self, x, mask):
        # patch embedding
        x = self.patchify(x)

        # mask out some patches according to the random mask
        x_vis, coords, vis_mask = self.apply_mask(x, mask, self.patches_resolution)

        # transformer forward
        for layer in self.layers:
            x_vis, coords, vis_mask = layer(x_vis, coords, vis_mask)
        x_vis = self.norm(x_vis)

        return x_vis


if __name__ == "__main__":
    '''Unit test for the PatchMerging module'''
    # resolution of the final stage
    H, W = 5, 5
    mask_ratio = 3 / 5.
    nvis, nmasked = int(H * W * (1 - mask_ratio)), int(H * W * mask_ratio)
    mask_new = torch.cat([torch.ones((nvis,)), torch.zeros((nmasked, ))])
    mask_new = mask_new[torch.randperm(H * W)].bool()

    # the second last stage
    mask = mask_new.reshape(H, 1, W, 1).repeat((1, 2, 1, 2)).reshape(-1)
    H, W = H * 2, W * 2
    x_ori = torch.arange(H * W) * 100.
    x = x_ori[mask].reshape(1, nvis * 4, -1)
    B, L, C = x.shape
    print(mask.reshape(H, W))
    print(x_ori.reshape(H, W))

    mask = mask.reshape(H//2, 2, W//2, 2).permute(0, 2, 1, 3).reshape(-1)
    coords = get_coordinates(H, W).reshape(2, -1).permute(1, 0)
    coords = coords.reshape(H//2, 2, W//2, 2, 2).permute(0, 2, 1, 3, 4).reshape(-1, 2)
    coords_vis_local = coords[mask].reshape(-1, 2)
    coords_vis_local = coords_vis_local[:, 0] * H + coords_vis_local[:, 1]
    idx_shuffle = torch.argsort(torch.argsort(coords_vis_local))

    # shuffle
    x = torch.index_select(x, 1, index=idx_shuffle)
    x = x.reshape(B, L//4, 4*C).squeeze()
    print(x.squeeze())